Tour-based Departure Time Models for Work and Non-work Tours of Workers

AbstractTiming of trips and tours play a very important role in travel demand modeling. To deal with issues related to congestion, the planner must have a clear understanding of the timing of travel patterns. In the literature, there is very little attention t o tour- based modeling in the developing country context. The inability of the current practice in evaluating substitution of trips and tours across intra-day periods in responses to time-varying policies (flexible and staggered work hours) and system attributes (congestion or tolls) needs to be addressed. To address these shortcomings, this study uses tours as the fundamental unit of analysis to circumvent discontinuity in mode choice and independence assumption in trip-based models. For this study, tour data from nearly 1000 workers are extracted from the Chennai Household Activity and Travel Survey 2004-05. Based on the information about the peak traffic, the 24 hour time-window was split into 6 time intervals. The nominal nature of the dependent variable is captured using Multinomial Logit Model (MNL). This paper investigates the timing decisions of tours in the context of Indian city, Chennai. Three related sub-objectives are pursued towards this goal: 1. develop models of tour timing (departure times) for workers, 2. analyze the role of individual, household, work-related, modal characteristics and transportation system attributes on this timing decision, and 3. understand the differences in behavior of tour timing between work tours and non-mandatory tours of workers

Vehicle class wise speed-volume models for heterogeneous traffic

Link performance functions commonly used for traffic assignment are often based on Volume Delay Functions (VDF) developed for homogeneous traffic. However, VDFs relating stream speed to the volume of traffic based on homogeneous lane-based traffic are not adequate for traffic assignment in developing countries due to the heterogeneous nature of traffic that is characterized by a mix of a wide range of vehicle classes with significant differences in static and dynamic characteristics and an imperfect lane discipline. Unfortunately, the use of VDFs similar to those for homogeneous traffic flow situations imposes strong restrictions considering two respects: 1) travel times at path and link levels can be obtained for an aggregated stream but not for individual vehicle types; 2) the effect of varying composition and asymmetric interactions is captured only to a limited extent by converting all vehicles into equivalent Passenger Car Unit (PCU). Hence, this paper proposes the development of VDFs specific to different classes of heterogeneous traffic, as it is more realistic in traffic assignment than the use of the same VDF for all classes of vehicles in a link. This study is aimed at developing models to determine the speed of each vehicle class as a function of flow and composition for six lane roads with heterogeneous traffic based on data obtained from Chennai city, India. Heterogeneity in this study mainly refers to differences in vehicle types (two-wheeler, car, bus, etc.) participating in mixed traffic. To develop multiple user class VDFs, the speed and flow of each vehicle class for a wide range of traffic flow conditions need to be recorded. As this is not possible using field measurements, an established micro-simulation model (HETEROSIM) is used for determining speeds for each vehicle type by systematically varying the volume and composition levels over a range of values that represent relevant and practical traffic conditions observed in six lane divided roads in Chennai city. The proposed delay functions are different from standard single user class VDFs in three key respects: first, they enable more realistic behaviour by modelling differences in class wise speeds at a given volume and composition level; second, they allow for capturing asymmetric interactions of different vehicle types on an average speed of a given vehicle class. Finally, speed-flow relationships for each class are also allowed to vary across volume levels which enable the representation of differential interactions at different levels of congestion in mixed traffic. The need for homogenizing the volumes in terms of a single class is obviated. The models significantly outperformed single class VDFs in both calibration and validation datasets. Further, the proposed models are used for analyzing heterogeneous traffic characteristics. Empirical evidence of asymmetric interactions and the impact of composition on classwise performance are also found and quantified. Finally, two applications of the proposed models are demonstrated for the level of service analysis of different classes and impact analysis of excluding some classes. The proposed models may have applications such as determining class wise road user costs and performance measures (e.g. emissions) that depend on class-specific speeds

A Comparison of Online and In-Person Activity Engagement: The Case of Shopping and Eating Meals

You're viewing a past Journal from the Good Systems Grand Challenge team at The University of Texas at Austin from February 2020.Office of the VP for Researc

Quantitative Description of Glycan-Receptor Binding of Influenza A Virus H7 Hemagglutinin

In the context of recently emerged novel influenza strains through reassortment, avian influenza subtypes such as H5N1, H7N7, H7N2, H7N3 and H9N2 pose a constant threat in terms of their adaptation to the human host. Among these subtypes, it was recently demonstrated that mutations in H5 and H9 hemagglutinin (HA) in the context of lab-generated reassorted viruses conferred aerosol transmissibility in ferrets (a property shared by human adapted viruses). We previously demonstrated that the quantitative binding affinity of HA to α2→6 sialylated glycans (human receptors) is one of the important factors governing human adaptation of HA. Although the H7 subtype has infected humans causing varied clinical outcomes from mild conjunctivitis to severe respiratory illnesses, it is not clear where the HA of these subtypes stand in regard to human adaptation since its binding affinity to glycan receptors has not yet been quantified. In this study, we have quantitatively characterized the glycan receptor-binding specificity of HAs from representative strains of Eurasian (H7N7) and North American (H7N2) lineages that have caused human infection. Furthermore, we have demonstrated for the first time that two specific mutations; Gln226→Leu and Gly228→Ser in glycan receptor-binding site of H7 HA substantially increase its binding affinity to human receptor. Our findings contribute to a framework for monitoring the evolution of H7 HA to be able to adapt to human host.National Institutes of Health (U.S.) (GM R37 GM057073-13)Singapore-MIT Alliance for Research and Technolog

Determinants of Glycan Receptor Specificity of H2N2 Influenza A Virus Hemagglutinin

The H2N2 subtype of influenza A virus was responsible for the Asian pandemic of 1957-58. However, unlike other subtypes that have caused pandemics such as H1N1 and H3N2, which continue to circulate among humans, H2N2 stopped circulating in the human population in 1968. Strains of H2 subtype still continue to circulate in birds and occasionally pigs and could be reintroduced into the human population through antigenic drift or shift. Such an event is a potential global health concern because of the waning population immunity to H2 hemagglutinin (HA). The first step in such a cross-species transmission and human adaptation of influenza A virus is the ability for its surface glycoprotein HA to bind to glycan receptors expressed in the human upper respiratory epithelia. Recent structural and biochemical studies have focused on understanding the glycan receptor binding specificity of the 1957-58 pandemic H2N2 HA. However, there has been considerable HA sequence divergence in the recent avian-adapted H2 strains from the pandemic H2N2 strain. Using a combination of structural modeling, quantitative glycan binding and human respiratory tissue binding methods, we systematically identify mutations in the HA from a recent avian-adapted H2N2 strain (A/Chicken/PA/2004) that make its quantitative glycan receptor binding affinity (defined using an apparent binding constant) comparable to that of a prototypic pandemic H2N2 (A/Albany/6/58) HA.National Institute of General Medical Sciences (U.S.) (GM57073)National Institute of General Medical Sciences (U.S.) (U54 GM62116)Singapore. Agency for Science, Technology and ResearchSingapore-MIT Alliance for Research and Technolog

Glycans as receptors for influenza pathogenesis

Influenza A viruses, members of the Orthomyxoviridae family, are responsible for annual seasonal influenza epidemics and occasional global pandemics. The binding of viral coat glycoprotein hemagglutinin (HA) to sialylated glycan receptors on host epithelial cells is the critical initial step in the infection and transmission of these viruses. Scientists believe that a switch in the binding specificity of HA from Neu5Acα2-3Gal linked (α2-3) to Neu5Acα2-6Gal linked (α2-6) glycans is essential for the crossover of the viruses from avian to human hosts. However, studies have shown that the classification of glycan binding preference of HA based on sialic acid linkage alone is insufficient to establish a correlation between receptor specificity of HA and the efficient transmission of influenza A viruses. A recent study reported extensive diversity in the structure and composition of α2-6 glycans (which goes beyond the sialic acid linkage) in human upper respiratory epithelia and identified different glycan structural topologies. Biochemical examination of the multivalent HA binding to these diverse sialylated glycan structures also demonstrated that high affinity binding of HA to α2-6 glycans with a characteristic umbrella-like structural topology is critical for efficient human adaptation and human-human transmission of influenza A viruses. This review summarizes studies which suggest a new paradigm for understanding the role of the structure of sialylated glycan receptors in influenza virus pathogenesis.National Institute of General Medical Sciences (U.S.) (Glue Grant U54 GM62116)National Institutes of Health (U.S.) (Grant GM57073)Singapore-MIT Alliance for Research and Technolog

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14